Confirming the validity of healthcare transactions has historically been a highly manual process that relies on human intuition for identifying unusual transactions based on countless hours of investigation. Investigators and health insurance payers endeavoring to detect fraudulent healthcare insurance payments have traditionally relied on manual calculations and/or ad-hoc analysis functions implemented in spreadsheet software tools based on analyses of insurance claims to detect fraud. For example, investigators would undertake an intuition-based “outlier analysis” to find claims that were somehow different than other claims that a health insurance payer had received in the past.
New improved systems have emerged that provide increased volumes of data that investigators and health insurance payers must analyze in order to effectively detect fraud. It is currently impossible for investigators and health insurance payers to analyze the data effectively without the aid of more advanced systems and tools.
Therefore, there is a need for systems that can analyze large volumes of data related to healthcare insurance payments and provide enhanced visual representations of the analysis via user interfaces for efficient and quick use by investigators and healthcare insurance payers. Finally, there is a need for an integrated representation of risk that takes into account multiple risk categories in a single score and a corresponding user-friendly visualization of healthcare events that give rise to the integrated representation of risk.